Choose the right model with Model Right Sizer

Model Right Sizer recommends the best AI model, reasoning effort, and token budget for each stage of a task, so you get the quality you need without defaulting to the largest model for everything. It is a Claude Code plugin available from the CloudZero marketplace.

The plugin evaluates your task in two passes: a blueprint before work starts that maps each stage to a recommended model tier, and a usage report after work finishes that compares actual token spend against the blueprint's predictions.

What you need

• Claude Code v2.1 or later

Install Model Right Sizer

1. Add the CloudZero marketplace to Claude Code. If you already added the marketplace when setting up AI Hub, skip this step.

claude plugin marketplace add cloudzero/cloudzero-claude-marketplace

2. Install the Model Right Sizer plugin.

claude plugin install model-right-sizer@cloudzero

3. (Optional) To have Claude consult the agent automatically on every substantive task in a specific repository, open a Claude Code session in that repository and invoke the model-right-sizer-install skill. The skill writes a dedicated section into the repository's CLAUDE.md file. It never modifies content outside that section and is safe to run more than once.

Use Model Right Sizer

Once installed, prompt the agent in natural language from any Claude Code session. The agent is read-only: it reads your project context and returns recommendations, but never edits or writes files.

Blueprint a task

Describe the work you plan to do. The agent breaks it into stages and recommends a model, reasoning effort, and token budget for each one. Not every stage needs the largest model; the blueprint shows you where a smaller, faster model is sufficient.

Example prompt: "Blueprint this PR: migrate the /api/billing endpoint from REST to GraphQL, add integration tests for the new schema, and update the API reference"

Review token spend

After completing work, ask for a usage report. The agent compares actual token spend and latency against the blueprint's predictions and flags stages that over- or under-shot, so you can refine your model choices for similar tasks in the future.

Example prompt: "Usage report on the billing API migration: we used Haiku for the tests and docs, Sonnet for the schema work"

Preview recommendations without building

To estimate token spend before committing to a task, use the dry-run skill. The agent analyzes the task and returns model recommendations for each stage without starting any work.

Example prompt: "Dry-run: add webhook support to the new billing GraphQL API"

How it works

Components

The plugin installs one agent and two skills:

Read-only architecture

The agent's tool access is limited to reading files and fetching public model pricing. It never edits or writes files. The model-right-sizer-install skill is the only component that writes anything, and its scope is limited to the dedicated section in CLAUDE.md described in step 3. The model-right-sizer-dryrun skill writes nothing.

Pricing verification

Model Right Sizer references current model pricing when making recommendations. The agent re-verifies the model lineup and prices against the model provider's pricing page each time it runs. If it cannot verify a price, it marks the figure as unverified in its output.

Direct query recommendations

Beyond model selection, the agent identifies stages where a direct data query would answer a question more reliably and at lower cost than a model call. When it detects this, it flags the stage and recommends using a query instead.

Multi-agent handoff optimization

For tasks that use multiple agents in sequence, Model Right Sizer recommends what data to pass between agents at each handoff. This keeps handoffs lean by preventing full conversation transcripts from being forwarded between stages.